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Add Llama2 Providers / Models
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63
g4f/Provider/DeepInfra.py
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63
g4f/Provider/DeepInfra.py
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from __future__ import annotations
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import json
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from aiohttp import ClientSession
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from ..typing import AsyncResult, Messages
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from .base_provider import AsyncGeneratorProvider
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class DeepInfra(AsyncGeneratorProvider):
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url = "https://deepinfra.com"
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working = True
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@classmethod
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async def create_async_generator(
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cls,
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model: str,
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messages: Messages,
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proxy: str = None,
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**kwargs
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) -> AsyncResult:
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if not model:
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model = "meta-llama/Llama-2-70b-chat-hf"
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headers = {
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"User-Agent": "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/118.0",
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"Accept": "text/event-stream",
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"Accept-Language": "de,en-US;q=0.7,en;q=0.3",
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"Accept-Encoding": "gzip, deflate, br",
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"Referer": f"{cls.url}/",
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"Content-Type": "application/json",
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"X-Deepinfra-Source": "web-page",
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"Origin": cls.url,
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"Connection": "keep-alive",
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"Sec-Fetch-Dest": "empty",
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"Sec-Fetch-Mode": "cors",
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"Sec-Fetch-Site": "same-site",
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"Pragma": "no-cache",
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"Cache-Control": "no-cache",
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}
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async with ClientSession(headers=headers) as session:
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data = {
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"model": model,
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"messages": messages,
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"stream": True,
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}
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async with session.post(
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"https://api.deepinfra.com/v1/openai/chat/completions",
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json=data,
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proxy=proxy
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) as response:
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response.raise_for_status()
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first = True
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async for line in response.content:
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if line.startswith(b"data: [DONE]"):
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break
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elif line.startswith(b"data: "):
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chunk = json.loads(line[6:])["choices"][0]["delta"].get("content")
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if chunk:
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if first:
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chunk = chunk.lstrip()
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if chunk:
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first = False
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yield chunk
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@ -6,15 +6,14 @@ from ..typing import AsyncResult, Messages
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from .base_provider import AsyncGeneratorProvider
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from .base_provider import AsyncGeneratorProvider
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models = {
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models = {
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"7B": {"name": "Llama 2 7B", "version": "d24902e3fa9b698cc208b5e63136c4e26e828659a9f09827ca6ec5bb83014381", "shortened":"7B"},
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"meta-llama/Llama-2-7b-chat-hf": {"name": "Llama 2 7B", "version": "d24902e3fa9b698cc208b5e63136c4e26e828659a9f09827ca6ec5bb83014381", "shortened":"7B"},
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"13B": {"name": "Llama 2 13B", "version": "9dff94b1bed5af738655d4a7cbcdcde2bd503aa85c94334fe1f42af7f3dd5ee3", "shortened":"13B"},
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"meta-llama/Llama-2-13b-chat-hf": {"name": "Llama 2 13B", "version": "9dff94b1bed5af738655d4a7cbcdcde2bd503aa85c94334fe1f42af7f3dd5ee3", "shortened":"13B"},
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"70B": {"name": "Llama 2 70B", "version": "2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", "shortened":"70B"},
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"meta-llama/Llama-2-70b-chat-hf": {"name": "Llama 2 70B", "version": "2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", "shortened":"70B"},
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"Llava": {"name": "Llava 13B", "version": "6bc1c7bb0d2a34e413301fee8f7cc728d2d4e75bfab186aa995f63292bda92fc", "shortened":"Llava"}
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"Llava": {"name": "Llava 13B", "version": "6bc1c7bb0d2a34e413301fee8f7cc728d2d4e75bfab186aa995f63292bda92fc", "shortened":"Llava"}
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}
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}
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class Llama2(AsyncGeneratorProvider):
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class Llama2(AsyncGeneratorProvider):
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url = "https://www.llama2.ai"
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url = "https://www.llama2.ai"
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supports_gpt_35_turbo = True
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working = True
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working = True
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@classmethod
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@classmethod
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@ -26,8 +25,8 @@ class Llama2(AsyncGeneratorProvider):
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**kwargs
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**kwargs
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) -> AsyncResult:
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) -> AsyncResult:
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if not model:
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if not model:
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model = "70B"
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model = "meta-llama/Llama-2-70b-chat-hf"
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if model not in models:
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elif model not in models:
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raise ValueError(f"Model are not supported: {model}")
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raise ValueError(f"Model are not supported: {model}")
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version = models[model]["version"]
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version = models[model]["version"]
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headers = {
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headers = {
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@ -54,7 +53,7 @@ class Llama2(AsyncGeneratorProvider):
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"systemPrompt": kwargs.get("system_message", "You are a helpful assistant."),
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"systemPrompt": kwargs.get("system_message", "You are a helpful assistant."),
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"temperature": kwargs.get("temperature", 0.75),
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"temperature": kwargs.get("temperature", 0.75),
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"topP": kwargs.get("top_p", 0.9),
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"topP": kwargs.get("top_p", 0.9),
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"maxTokens": kwargs.get("max_tokens", 1024),
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"maxTokens": kwargs.get("max_tokens", 8000),
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"image": None
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"image": None
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}
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}
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started = False
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started = False
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@ -68,9 +67,9 @@ class Llama2(AsyncGeneratorProvider):
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def format_prompt(messages: Messages):
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def format_prompt(messages: Messages):
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messages = [
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messages = [
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f"[INST]{message['content']}[/INST]"
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f"[INST] {message['content']} [/INST]"
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if message["role"] == "user"
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if message["role"] == "user"
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else message["content"]
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else message["content"]
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for message in messages
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for message in messages
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]
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]
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return "\n".join(messages)
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return "\n".join(messages) + "\n"
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@ -17,6 +17,7 @@ from .ChatgptFree import ChatgptFree
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from .ChatgptLogin import ChatgptLogin
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from .ChatgptLogin import ChatgptLogin
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from .ChatgptX import ChatgptX
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from .ChatgptX import ChatgptX
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from .Cromicle import Cromicle
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from .Cromicle import Cromicle
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from .DeepInfra import DeepInfra
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from .FakeGpt import FakeGpt
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from .FakeGpt import FakeGpt
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from .FreeGpt import FreeGpt
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from .FreeGpt import FreeGpt
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from .GPTalk import GPTalk
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from .GPTalk import GPTalk
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@ -70,6 +71,7 @@ class ProviderUtils:
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'ChatgptX': ChatgptX,
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'ChatgptX': ChatgptX,
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'CodeLinkAva': CodeLinkAva,
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'CodeLinkAva': CodeLinkAva,
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'Cromicle': Cromicle,
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'Cromicle': Cromicle,
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'DeepInfra': DeepInfra,
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'DfeHub': DfeHub,
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'DfeHub': DfeHub,
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'EasyChat': EasyChat,
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'EasyChat': EasyChat,
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'Equing': Equing,
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'Equing': Equing,
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@ -144,6 +146,7 @@ __all__ = [
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'ChatgptLogin',
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'ChatgptLogin',
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'ChatgptX',
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'ChatgptX',
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'Cromicle',
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'Cromicle',
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'DeepInfra',
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'CodeLinkAva',
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'CodeLinkAva',
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'DfeHub',
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'DfeHub',
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'EasyChat',
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'EasyChat',
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@ -6,12 +6,14 @@ from .Provider import (
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GptForLove,
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GptForLove,
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ChatgptAi,
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ChatgptAi,
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GptChatly,
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GptChatly,
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DeepInfra,
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ChatgptX,
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ChatgptX,
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ChatBase,
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ChatBase,
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GeekGpt,
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GeekGpt,
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FakeGpt,
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FakeGpt,
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FreeGpt,
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FreeGpt,
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NoowAi,
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NoowAi,
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Llama2,
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Vercel,
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Vercel,
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Aichat,
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Aichat,
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GPTalk,
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GPTalk,
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@ -74,6 +76,21 @@ gpt_4 = Model(
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])
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])
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)
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)
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llama2_7b = Model(
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name = "meta-llama/Llama-2-7b-chat-hf",
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base_provider = 'huggingface',
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best_provider = RetryProvider([Llama2, DeepInfra]))
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llama2_13b = Model(
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name ="meta-llama/Llama-2-13b-chat-hf",
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base_provider = 'huggingface',
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best_provider = RetryProvider([Llama2, DeepInfra]))
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llama2_70b = Model(
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name = "meta-llama/Llama-2-70b-chat-hf",
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base_provider = "huggingface",
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best_provider = RetryProvider([Llama2, DeepInfra]))
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# Bard
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# Bard
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palm = Model(
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palm = Model(
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name = 'palm',
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name = 'palm',
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